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- W4377130661 abstract "CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding-boxes for one face. NMS is used to filter out inaccurate candidate boxes to get the most accurate box. The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score. However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster. In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes. To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods. Our method performs well on low-quality and tiny face samples. Experiments demonstrate that our method is effective as a post-processor for different face detection methods." @default.
- W4377130661 created "2023-05-21" @default.
- W4377130661 creator A5021449354 @default.
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- W4377130661 date "2023-05-17" @default.
- W4377130661 modified "2023-09-29" @default.
- W4377130661 title "Inverted Non-maximum Suppression for more Accurate and Neater Face Detection" @default.
- W4377130661 doi "https://doi.org/10.48550/arxiv.2305.10593" @default.
- W4377130661 hasPublicationYear "2023" @default.
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